https://x.com/ChujieZheng/status/2039909917323383036 is the pre-release poll they did. ~397B was not a listed choice and plenty of people took it as a signal that it might not be up for release.

A Qwen research member had a poll on X asking what Qwen 3.6 sizes people wanted to see:

https://x.com/ChujieZheng/status/2039909917323383036

Likely to drive engagement, but the poll excluded the large model size.

17b per token. So when you’re generating a single stream of text (“decoding”) 17b parameters are active.

If you’re decoding multiple streams, it will be 17b per stream (some tokens will use the same expert, so there is some overlap).

When the model is ingesting the prompt (“prefilling”) it’s looking at many tokens at once, so the number of active parameters will be larger.

397B params, 17B activated at the same time

Those 17B might be split among multiple experts that are activated simultaneously

That's not how it works. Many people get confused by the “expert” naming, when in reality the key part of the original name “sparse mixture of experts” is sparse.

Experts are just chunks of each layers MLP that are only partially activated by each token, there are thousands of “experts” in such a model (for Qwen3-30BA3, it was 48 layers x 128 “experts” per layer with only 8 active at each token)

The point is that open weights turns puts inference on the open market, so if your model is actually good and providers want to serve it, it will drive costs down and inference speeds up. Like Cerebras running Qwen 3 235B Instruct at 1.4k tps for cheaper than Claude Haiku (let that tps number sink in for a second. For reference, Claude Opus runs ~30-40 tps, Claude Haiku at ~60. Several orders of magnitude difference). As a company developing models, it means you can't easily capture the inference margins even though I believe you get a small kickback from the providers.

So I understand why they wouldn't want to go open weight, but on the other hand, open weight wins you popularity/sentiment if the model is any good, researchers (both academic and other labs) working on your stuff, etc etc. Local-first usage is only part of the story here. My guess is Qwen 3.5 was successful enough that now they want to start reaping the profits. Unfortunately most of Qwen 3.5's success is because it's heavily (and successfully!) optimized for extremely long-context usage on heavily constrained VRAM (i.e. local) systems, as a result of its DeltaNet attention layers.

You can rent a cloud H200 with 140GB VRAM in a server with 256GB system ram for $3-4/hr.

The 397B model can be run at home with the weights stored on an SSD (or on 2 SSDs, for double throughput).

Probably too slow for chat, but usable as a coding assistant.

OpenRouter.

Running the mxfp4 unsloth quant of qwen3.5-397b-a17b, I get 40 tps prefill, 20tps decode.

AMD threadripper pro 9965WX, 256gb ddr5 5600, rtx 4090.

I'm running it on dual DGX Sparks.

It doesn't matter how many can run it now, it's about freedom. Having a large open weights model available allows you to do things you can't do with closed models.

I can (barely, but sustainably) run Q3.5 397B on my Mac Studio with 256GB unified. It cost $10,000 but that's well within reach for most people who are here, I expect.